Papers by Meng Qu
Knowledge Graph Embedding with Hierarchical Relation Structure (D18-1)
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| Challenge: | Existing knowledge graph embedding models embed entities and relations into latent vectors without leveraging rich information from relation structure. |
| Approach: | They extend existing KGE models to learn knowledge representations by leveraging relation structure . authors say their approach is capable of extending other KGEs . |
| Outcome: | The proposed approach can extend existing KGE models, and validates against baselines. |
Towards Unifying Multi-Lingual and Cross-Lingual Summarization (2023.acl-long)
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| Challenge: | Existing work on multilingual summarization and cross-lingual summmarization has been limited due to their different definitions. |
| Approach: | They propose to unify MLS and CLS into a more general setting, i.e. many-to-many summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art models in the zero-shot directions. |
Recurrent Event Network: Autoregressive Structure Inferenceover Temporal Knowledge Graphs (2020.emnlp-main)
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| Challenge: | Existing methods for reasoning over temporal knowledge graphs focus on past timestamps and are not able to predict future interactions. |
| Approach: | They propose a novel autoregressive architecture for predicting future interactions using a recurrent event encoder and a neighborhood aggregator. |
| Outcome: | The proposed method achieves state-of-the-art on five public datasets. |
Collaborative Policy Learning for Open Knowledge Graph Reasoning (D19-1)
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| Challenge: | Existing models of knowledge graph reasoning suffer from limited performance when working on sparse and incomplete graphs due to the lack of evidential paths that can reach target entities. |
| Approach: | They propose a framework to train two collaborative agents to reason for missing facts over a graph augmented by a text corpus. |
| Outcome: | Experiments on two public datasets show the proposed approach is effective on a knowledge graph reasoning task. |
A Survey on Cross-Lingual Summarization (2022.tacl-1)
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| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
| Approach: | They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other . |
| Outcome: | The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis. |
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing approaches to building cross-lingual summarization systems on dialogue documents are limited. |
| Approach: | They propose a benchmark dataset for building cross-lingual summarization systems on dialogue documents. |
| Outcome: | The proposed model outperforms pipeline models on ClidSum and mDialBART. |
Unlocking Anticipatory Text Generation: A Constrained Approach for Large Language Models Decoding (2024.emnlp-main)
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| Challenge: | Large language models have shown a powerful ability for text generation, but undesired behaviors such as toxicity and hallucinations can manifest. |
| Approach: | They propose to formalize text generation as a future-constrained generation problem to minimize undesirable behaviors and enforce faithfulness to instructions. |
| Outcome: | The proposed approach is effective across three tasks, including keyword-constrained generation, toxicity reduction, and factual correctness in question-answering. |